Neural Network

Neural networks are computational models inspired by the structure and function of the brain, primarily aimed at approximating complex functions and solving diverse problems through learning from data. Current research emphasizes improving efficiency and robustness, exploring novel architectures like sinusoidal neural fields and hybrid models combining neural networks with radial basis functions, as well as developing methods for understanding and manipulating the internal representations learned by these networks, such as through hyper-representations of network weights. These advancements are driving progress in various fields, including computer vision, natural language processing, and scientific modeling, by enabling more accurate, efficient, and interpretable AI systems.

Papers

May 31, 2024

May 30, 2024

May 29, 2024

May 28, 2024

May 27, 2024

Building a temperature forecasting model for the city with the regression neural network (RNN)
Nguyen Phuc Tran, Duy Thanh Tran, Thi Thuy Nga Duong
Neural Network Recurrent Neural Network Predictive Model Novel Regression Urban Environment \Sigma}{\Delta}$ Low Pas RNN Weather Prediction Model Accurate Predictive Model Urban Temperature

Neural Network

Papers

Predicting ptychography probe positions using single-shot phase retrieval neural network

Solving partial differential equations with sampled neural networks

Advancing Financial Risk Prediction Through Optimized LSTM Model Performance and Comparative Analysis

Optimizing cnn-Bigru performance: Mish activation and comparative analysis with Relu

Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice

Flexible SE(2) graph neural networks with applications to PDE surrogates

Tropical Expressivity of Neural Networks

Symmetries in Overparametrized Neural Networks: A Mean-Field View

Semantic Landmark Detection & Classification Using Neural Networks For 3D In-Air Sonar

Understanding and Minimising Outlier Features in Neural Network Training

Convex neural network synthesis for robustness in the 1-norm

Few-Shot Testing: Estimating Uncertainty of Memristive Deep Neural Networks Using One Bayesian Test Vector

Learning Mixture-of-Experts for General-Purpose Black-Box Discrete Optimization

Semiring Activation in Neural Networks

Scalable Surrogate Verification of Image-based Neural Network Control Systems using Composition and Unrolling

SGD method for entropy error function with smoothing l0 regularization for neural networks

Classifying Overlapping Gaussian Mixtures in High Dimensions: From Optimal Classifiers to Neural Nets

Self-Supervised Dual Contouring

$C^2M^3$: Cycle-Consistent Multi-Model Merging

Building a temperature forecasting model for the city with the regression neural network (RNN)